Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language Translation (IWSLT), 201

Direct Speech-to-Text Translation Models as Students of Text-to-Text Models

Direct speech-to-text translation (ST) is an emerging approach that consists in performing the ST task with a single neural model. Although this paradigm comes with the promise to outperform the traditional pipeline systems, its rise is still limited by the paucity of speech-translation paired corpora compared to the large amount of speech-transcript and parallel bilingual corpora available to train previous solutions. As such, the research community focused on techniques to transfer knowledge from automatic speech recognition (ASR) and machine translation (MT) models trained on huge datasets. In this paper, we extend and integrate our recent work (Gaido, Gangi, et al. 2020) analysing the best performing approach to transfer learning from MT, which is represented by knowledge distillation (KD) in sequence-to-sequence models. After the comparison of the different KD methods to understand which one is the most effective, we extend our previous analysis of the effects – both in terms of benefits and drawbacks – to different language pairs in high-resource conditions, ensuring the generalisability of our findings. Altogether, these extensions complement and complete our investigation on KD for speech translation leading to the following overall findings: i) the best training recipe involves a word-level KD training followed by a fine-tuning step on the ST task, ii) word-level KD from MT can be detrimental for gender translation and can lead to output truncation (though these problems are alleviated by the fine-tuning on the ST task), and iii) the quality of the ST student model strongly depends on the quality of the MT teacher model, although the correlation is not linear

Chemical inhibitor targeting the replication protein A-DNA interaction increases the efficacy of Pt-based chemotherapy in lung and ovarian cancer

Platinum-based chemotherapeutics exert their therapeutic efficacy via the formation of DNA adducts which interfere with DNA replication, transcription and cell division and ultimately induce cell death. Repair and tolerance of these Pt-DNA lesions by nucleotide excision repair (NER) and homologous recombination (HR) can substantially reduce the effectiveness of therapy. Inhibition of these repair pathways, therefore, holds the potential to sensitize cancer cells to Pt treatment and increase clinical efficacy. Replication Protein A (RPA) plays essential roles in both NER and HR, along with its role in DNA replication and DNA damage checkpoint activation. Each of these functions is, in part, mediated by RPA binding to single-stranded DNA (ssDNA). Here we report the synthesis and characterization of novel derivatives of RPA small molecule inhibitors and their activity in models of epithelial ovarian cancer (EOC) and non-small cell lung cancer (NSCLC). We have synthesized analogs of our previously reported RPA inhibitor TDRL-505 and determined the structure-activity relationships. These data led us to the identification of TDRL-551, which exhibited a greater than 2-fold increase in in vitro activity. TDRL-551 showed synergy with Pt in tissue culture models of EOC and in vivo efficacy, as a single agent and in combination with platinum, in a NSCLC xenograft model. These data demonstrate the utility of RPA inhibition in EOC and NSCLC and the potential in developing novel anticancer therapeutics that target RPA-DNA interactions

Low Resource Neural Machine Translation: A Benchmark for Five African Languages

Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS]). We collected the available resources on the SATOS languages to evaluate the current state of NMT for LRLs. Our evaluation, comparing a baseline single language pair NMT model against semi-supervised learning, transfer learning, and multilingual modeling, shows significant performance improvements both in the En-LRL and LRL-En directions. In terms of averaged BLEU score, the multilingual approach shows the largest gains, up to +5 points, in six out of ten translation directions. To demonstrate the generalization capability of each model, we also report results on multi-domain test sets. We release the standardized experimental data and the test sets for future works addressing the challenges of NMT in under-resourced settings, in particular for the SATOS languages.Comment: Accepted for AfricaNLP workshop at ICLR 202

Report on Characterization of U-10 wt.% Zr Alloy

This report summarizes the chemical and structural characterization results for a U-10 wt.% Zr alloy to be used in an ultra-high burn-up nuclear fuel concept. The as-cast alloy material was received from Texas A and M University. Characterization and an initial heat treatment of the alloy material were conducted at Lawrence Livermore National Laboratory. The as-received ingot was sectioned for X-ray analysis, metallography, SEM, TEM, and heat treatments, as shown in Figure 1

High accuracy short-term PWV operational forecast at the VLT and perspectives for sky background forecast

In this paper we present the first results ever obtained by applying the autoregressive (AR) technique to the precipitable water vapour (PWV). The study is performed at the Very Large Telescope. The AR technique has been recently proposed to provide forecasts of atmospheric and astroclimatic parameters at short time scales (up to a few hours) by achieving much better performances with respect to the 'standard forecasts' provided early afternoon for the coming night. The AR method uses the real-time measurements of the parameter of interest to improve the forecasts performed with atmospherical models. We used here measurements provided by LHATPRO, a radiometer measuring continuously the PWV at the VLT. When comparing the AR forecast at 1h to the standard forecast, we observe a gain factor of $\sim$ 8 (i.e. $\sim$ 800 per cent) in terms of forecast accuracy. In the PWV $\leq$ 1 mm range, which is extremely critical for infrared astronomical applications, the RMSE of the predictions is of the order of just a few hundredth of millimetres (0.04 mm). We proved therefore that the AR technique provides an important benefit to VLT science operations for all the instruments sensitive to the PWV. Besides, we show how such an ability in predicting the PWV can be useful also to predict the sky background in the infrared range (extremely appealing for METIS). We quantify such an ability by applying this method to the NEAR project (New Earth in the Alpha Cen region) supported by ESO and Breakthrough Initiatives

An Assessment of the Net Value of CSP Systems Integrated with Thermal Energy Storage

AbstractWithin this study, we evaluate the operational and capacity value—or total system value—for multiple concentrating solar power (CSP)plant configurations under an assumed 33% renewable penetration scenario in California. We calculate the first-year bid price for two CSP plants, including a 2013 molten-salt tower integrated with a conventional Rankine cycle and a hypothetical 2020 molten-salt tower system integrated with an advanced supercritical carbon-dioxide power block. The overall benefit to the regional grid, defined in this study as the net value, is calculated by subtracting the first-year bid price from the total system value.Re--sults of this study indicate a positive net value for a variety of scenarios, depending on technology assumptions and assumed values for natural gas price and tax incentives. We provide results for the 2013 and 2020 CSP configurations as a function of thermal energy storage capacity and solar field size. We provide a sensitivity of these results to natural gas price, which influence the operation value and thus the total system value. We also investigate the sensitivity of the net value to current and anticipated tax incentives

CFD Ablation Predictions with Coupled GSI Modeling for Charring and non-Charring Materials

To this day, a major objective of TPS design is to reduce empiricism, and to increase fundamental modeling capability through increased understanding. One of the most challenging aspect is the proper coupling between the material response and the external flow field. With this regard, the goal of this research activity is the improvement of the numerical modeling capabilities through the development of advanced CFD tools integrated with Gas-Surface Interaction (GSI) modeling. Numerical prediction of ablation is ambitious and cpu-time demanding due to the complex multiphase physical and chemical processes that occur. With improvements in computational algorithms and advances in computer hardware, Navier- Stokes based approaches have become the norm in recent years for coupling to material thermal response predictions. The present state of the art in fluid-material coupling is represented by loose coupling of a high-fidelity CFD flow solver with a material thermal response code. In that respect, some major restrictions are still present in these state of the art coupled solutions: surface chemical equilibrium assumption non-ablating flow field prediction simplified diffusion modeling based on transfer coefficient Chemical equilibrium is a special condition of the general chemical nonequilibrium condition and surface recession rate predicted by the chemical equilibrium surface chemistry is usually reasonably conservative and is considered to be a best alternative when the nonequilibrium computation is too expensive or unlikely to be achieved. The ablation models are currently largely based on the surface equilibrium assumption and the effects and importance of non-equilibrium ablation models coupled with CFD tools are only beginning to be explored. Moreover, the coupling between CFD solver and material response code is often made considering non-ablating flow field solutions assuming a fully/super-catalytic, radiative equilibrium wall. This means that the effect on the flow field solution of the ablation and pyrolysis gas injection and of variable surface temperature are treated only approximately relying on the use of mass and energy transfer coefficients and semi-empirical blowing correction equations. Finally, the ablation rate is generally computed by the material response code using thermochemical tables and extremely simplified diffusion models based on transfer coefficients and semi-empirical relations relating mass and energy transfer. The objective of this research activity is to remove these major limiting assumptions developing suitable finite-rate GSI models and integrating CFD technology with Computational Surface Thermochemistry (CST) to take into account the effect of surface ablation and pyrolysis gas injection on the flow field and to allow surface ablation and surface temperature distributions to be determined as part of the CFD solution. Because the entire flow field is to be solved with ablative boundary conditions, the film-transfer theory assumption is no longer needed; this will permit to avoid all of the classical approximations such as transfer coefficients, equilibrium thermochemical tables, and blowing correction equations which needs to be used when ablative boundary conditions are not accounted for in the CFD solution. The ablative boundary conditions, based on finite-rate chemistry, species mass conservation and surface energy balance, is discretized and integrated with the CFD code to predict aerothermal heating, surface temperature, gas-phase surface composition, and surface ablation rate. The concentrations of chemical species at wall are determined from finite-rate gas-surface chemical reactions balanced by mass transfer rate. The surface temperature is determined from the surface energy balance assuming steady-state ablation or coupling with a thermal response code. The surface recession rate and the surface temperature are thus obtained as part of the flow field solution. The computational tool developed in this work is used to simulate two sets of experimental data for nozzle material ablation: sub-scale motor tests carried out for the Space Shuttle Reusable Solid Rocket Motor and the static firing tests of the second and third stage solid rocket motors of the European VEGA launcher which use carbon-carbon for the throat insert and carbon-phenolic for the region downstream of the throat